Yao Huang
Titles as the sources gave them. In 1 entry, with 3 checked quotations.
What they said
Newest first. Each quotation was found word for word in the source.
“such single-direction formulations generalize poorly across datasets and suffer substantial performance degradation when applied to heterogeneous deceptive scenarios, indicating that internal representations of deception are not universally aligned across domains.”
“with just 10 adaptation examples, it achieves an average AUROC of 0.97 and a recall of 95% at a 5% FPR across evaluated datasets, significantly outperforming existing static probing methods.”
“Although Qwen3-32B shows increased confusion on transaction-related insider scenarios due to partial representation overlap, our approach still surpasses all baselines by a substantial margin.”
In the Base
Every entry they appear in.
Why Static Probes Fail: Context Determines Deception Feature Composition
Yao Huang and co-authors argue that detecting AI deception with a single linear probe in a model's activations does not generalize across settings. They propose a multi-direction method that adapts to each setting from about 10 examples, and report better detection than single-probe baselines on two open-weight models.