A Lie Detector Test for Language Models: Reading Knowledge a Model Won't Reveal
Large language models can hold knowledge they do not report. A model may sandbag on a capability evaluation, or answer against what it internally knows, and its outputs alone cannot tell whether it is hiding an answer or simply does not have one. We borrow the Concealed Information Test, a forensic method that identifies guilty knowledge by presenting a suspect with the true detail among plausible decoys and measuring a stronger response to the item they recognize. Our method, Probe of Internal Recognition (PIR), does the same inside a model. It presents a question with its candidate answers and reads, from the model's internal states, which candidate the model recognizes as correct. PIR ...
| Authors | Hiskias Dingeto |
|---|---|
| Primary category | cs.AI |
| Published | 18 September 2026 |
| Last updated at source | 23 September 2026 |
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