The paper argues that single‑direction probes are insufficient for detecting deception in large language models because deception manifests in multiple, context‑dependent facets. It proposes modeling deception as a multi‑dimensional subspace and uses iterative orthogonal decomposition to uncover complementary directions. A domain‑adaptive gating mechanism then dynamically composes these directions with few‑shot adaptation, achieving high AUROC and recall across diverse datasets.
By Yao Huang, Siyuan Chen, Zhenghan Zhou, Yinpeng Dong
arXiv:2607. 20479v1 Announce Type: new Abstract: Training probes to detect deceptive outputs from large language models is still an open problem.
By Amr Moustafa, Max Feser, Florian Mai
arXiv:2610.12445v2 Announce Type: replace-cross
Abstract: Recent incidents have highlighted the challenge of monitoring LLM agents and the danger of models deceiving people. We show that white-box de...
By Oskar J. Hollinsworth, Alex F. Spies, Tigist Diriba, Adam Gleave, Chris Cundy
The paper presents a white‑box deception detection system that uses probes trained on the largest deception dataset (FIBS) and a novel architecture aggregating across layers and tokens. The probes achieve 98.8% AUC on SHADE‑Arena, outperforming a text‑monitoring baseline, and reach up to 99.7% AUC when detecting introspective deception that cannot be inferred from context alone. They also effectively detect deception in open‑weight models on politically sensitive topics and when models are pressured about their beliefs.
By Oskar J. Hollinsworth, Alex F. Spies, Tigist Diriba, Adam Gleave, Chris Cundy
arXiv:2602. 01425v2 Announce Type: replace Abstract: Linear probes are a promising approach for monitoring AI systems for deceptive behaviour.
By Vikram Natarajan, Devina Jain, Shivam Arora, Satvik Golechha, Joseph Bloom
arXiv:2607. 04222v1 Announce Type: new Abstract: Interpretability methods aim to reveal the features represented inside large language models (LLMs).
By Amit LeVi, Elad David, Max Fomin