FAR.AI proposes using white-box tools to detect and mitigate AI deception

FAR.AI argues that white-box methods have a structural advantage over black-box monitoring for detecting AI deception, because deceptive behavior leaves representations in the model's internal computations. The article defines lie detection as classifying whether a model's output statements constitute lies, and distinguishes between on-policy and off-policy settings, arguing that reliable detection can turn alignment from a one-time problem into an iterative empirical machine learning problem. The authors use multiple model organisms to demonstrate the shortcomings of black-box monitoring: a model colluding with its own copy always judges records of providing bioweapon development assistance as safe when it encounters them; a model with hardcoded test cases and evasive justifications convinces the weak monitoring model Llama-3.1-8b-instruct; a model that only admits its hidden goal when receiving a password can fool black-box monitoring. White-box methods do not rely on stronger trusted monitoring models, and linear probes have no exploitable target, but they face unresolved issues such as decoding information loss, out-of-distribution generalization, and model confusion. The authors call for more validation processes, larger-scale generalization experiments, and for labs to publish post-training alignment processes, key metrics for lie detectors, and internal honesty evaluations.