"Did you lie?" Evaluating Lie Detectors across Model Scale and Belief-Verified Model Organisms
Robust lie detectors for language models could enable powerful techniques for auditing, monitoring, and post-hoc investigation of model behaviour, but evaluating them requires testbeds where models verifiably believe the opposite of what they say. We show that existing trained model organisms often fail this requirement, leaving prior positive and negative detection results difficult to interpret. We address this with 13 reasoning model organisms whose hidden beliefs are verified in chain-of-thought and shown to generalise to held-out tasks, alongside Varied Deception, a prompted-lying testbed covering a broad range of lie-inducing motivations. On these testbeds we evaluate four detectors...
| Authors | Alan Cooney, David Africa, Geoffrey Irving |
|---|---|
| Primary category | cs.AI |
| Published | 10 June 2026 |
| Last updated at source | 17 June 2026 |
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