arXiv:2610.07967v1 Announce Type: new Abstract: As large language model (LLM) agents become increasingly autonomous, they may pursue task performance through deception, raising concerns about their r...
arXiv:2608. 08881v1 Announce Type: new Abstract: The current work developed seven Retrieval-Augmented Generation (RAG) models based on leading deception theories and compared how deception judgments were made relative to baseline models.
The study reviews 25 years of automated verbal deception detection research, analyzing 3,653 models across 97 datasets. Pooled accuracy is 74.4% (95% CI: 71.2%-77.4%) with high heterogeneity, driven more by methodological quality than model complexity. Only a small fraction of studies use verifiable ground truth or independent evaluation, and the accuracy ceiling of 70‑75% appears unlikely to improve under current practices.
arXiv:2602. 01425v2 Announce Type: replace Abstract: Linear probes are a promising approach for monitoring AI systems for deceptive behaviour.
arXiv:2606. 17478v1 Announce Type: cross Abstract: As LLMs acquire stronger reasoning capabilities, deceptive behavior becomes an increasingly serious safety concern.
The paper reviews 25 years of automated verbal deception detection research, analyzing 289 reports and 3,653 models across 97 datasets. It finds a pooled accuracy of 74.4% with high heterogeneity, driven mainly by methodological quality rather than model complexity. Despite the use of embeddings and large language models, predictive performance has not improved, and only a small fraction of studies use verifiable ground truth or independent evaluation data.
arXiv:2607. 20444v1 Announce Type: cross Abstract: Large language models (LLMs) can produce deceptive responses: outputs that mislead users in service of a contextually or experimentally induced goal.
arXiv:2606. 12618v1 Announce Type: new Abstract: 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.
arXiv:2607. 20479v1 Announce Type: new Abstract: Training probes to detect deceptive outputs from large language models is still an open problem.
arXiv:2609.21996v1 Announce Type: new Abstract: 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...
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.
The study examines how large language models, specifically Llama‑3.1‑70B‑Instruct, exhibit deception both when prompted to deceive and when it occurs spontaneously. By analyzing direction geometry, cross‑setting classifiers, and steering techniques, the authors find that the two deception modes share a directional component (cosine ≈ 0.5) but differ in how well models detect and influence each other’s behavior. Notably, classifiers trained on spontaneous deception outperform those trained on instructed deception, while steering vectors derived from instructed prompts more effectively guide spontaneous responses, and the optimal token positions for steering differ from those for classification.