arXiv AI June 12, 2026 By Alan Cooney, David Africa, Geoffrey Irving

"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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

llms

arXiv AI

Sep 21

A Lie Detector Test for Language Models: Reading Knowledge a Model Won't Reveal

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...

By Hiskias Dingeto

llms

arXiv AI

Jun 17

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.

By Kexin Chen, Yi Liu, Haonan Zhang, Yanhui Li, Xinyu Deng, Dongxia Wang

llms safety

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.

By Amr Moustafa, Max Feser, Florian Mai

llms efficiency benchmarks

arXiv AI

Jun 17

Rift: A Conflict Signature for Deception in Language Models

arXiv:2606. 17229v1 Announce Type: cross Abstract: A model that lies while knowing the truth is the central case ELK cannot handle with behavioral evaluation alone.

By Petr Nyoma

llms agents fine-tuning safety

arXiv AI

Jul 10

What LLM Forecasters Know but Don't Say: Probing Internal Representations for Calibration and Faithfulness

arXiv:2607. 08046v1 Announce Type: cross Abstract: Large language models fine-tuned for forecasting can be accurate yet poorly calibrated, and their chain-of-thought (CoT) reasoning may not faithfully reflect the evidence behind a forecast.

By Rapha\"el Sarfati, Pratyush Ranjan Tiwari, Siddharth Boppana, Christopher J. Earls, Srikar Varadaraj, Eric Ho

llms fine-tuning benchmarks

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.

By Hiskias Dingeto

safety

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Related stories

A Lie Detector Test for Language Models: Reading Knowledge a Model Won't Reveal

Decoding Hidden Deception in Reasoning LLMs: Activation Explainers for Deception Auditing

Beyond Liars' Bench: The Impact of Lie Typology, Depth, and Sparsity on Deception Detection in LLMs

Rift: A Conflict Signature for Deception in Language Models

What LLM Forecasters Know but Don't Say: Probing Internal Representations for Calibration and Faithfulness

Train the Model, Not the Reader: Decodability Supervision for Verifiable Activation Explanations