arXiv AI
Jun 19
One Probe Won't Catch Them All: Towards Targeted Deception Detection
arXiv:2602. 01425v2 Announce Type: replace Abstract: Linear probes are a promising approach for monitoring AI systems for deceptive behaviour.
Decoding Hidden Deception in Reasoning LLMs: Activation Explainers for Deception Auditing
arXiv:2606. 17478v1 Announce Type: cross Abstract: As LLMs acquire stronger reasoning capabilities, deceptive behavior becomes an increasingly serious safety concern.
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arXiv AI
Jul 24
Beyond Liars' Bench: The Impact of Lie Typology, Depth, and Sparsity on Deception Detection in LLMs
arXiv:2607. 20479v1 Announce Type: new Abstract: Training probes to detect deceptive outputs from large language models is still an open problem.
arXiv AI
Jul 24
Confidently Deceptive: How Confidence Amplifies the Risk of LLM Deception
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 Machine Learning
Jun 5
Grounded but Misleading: Evaluating Semantic Alignment in AI-Generated Security Explanations
arXiv:2602. 05056v2 Announce Type: replace-cross Abstract: Online scams increasingly leverage fluent and context-aware social engineering strategies, creating growing demand for AI systems that explain why a message may be risky.
arXiv AI
Jun 12
"Did you lie?" Evaluating Lie Detectors across Model Scale and Belief-Verified Model Organisms
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 AI
Jul 28
Traceable LLM Reasoning for Fake-Order Fraud Detection
arXiv:2607. 23075v1 Announce Type: cross Abstract: Detecting fake-order fraud at scale remains a critical challenge for large online-to-offline (O2O) service platforms, as existing approaches often rely on expert-designed features, produce black-box decisions, and provide limited interpretability.
arXiv AI
Jul 23
Train the Model, Not the Reader: Decodability Supervision for Verifiable Activation Explanations
arXiv:2607. 20379v1 Announce Type: new Abstract: Natural-language autoencoders score explanations of hidden activations by reconstruction: an explanation is deemed faithful if the activation can be regenerated from it.
arXiv AI
Jun 8
Stable Reasoning, Unstable Responses: Mitigating LLM Deception via Stability Asymmetry
arXiv:2603. 26846v2 Announce Type: replace-cross Abstract: As Large Language Models (LLMs) expand in capability and application scope, their trustworthiness becomes critical.
arXiv AI
Jun 10
Janus: A Benchmark for Goal-Conditioned Information Distortion in LLMs
arXiv:2606. 10852v1 Announce Type: cross Abstract: LLM deception is often evaluated through direct markers such as fabricated claims, explicit lies, or strategic concealment.
arXiv Machine Learning
Aug 10
Latent Fact-Checking: Detecting Misinformation through Activation Engineering
arXiv:2608. 06417v1 Announce Type: new Abstract: The proliferation of misinformation online has driven demand for scalable detection systems.
arXiv AI
Jul 17
Transcoders for Investigating Deception in Language Models
arXiv:2607. 14791v1 Announce Type: new Abstract: Transcoders have recently emerged as a promising approach for mechanistic interpretability (MI), enabling circuit-level analysis of model behaviour.
arXiv AI
Jun 29
PRISON: Unmasking the Criminal Potential of Large Language Models
arXiv:2506. 16150v4 Announce Type: replace-cross Abstract: As large language models (LLMs) advance, concerns about their misconduct in complex social contexts intensify.