[Paper] Propensities Please: Exposing Alignment-Faking LLMs using Synthetic Document Finetuning

This research was completed in context of the LASR Labs Fellowship Summer 2026, by Ardy Haroen, Florian Lorkowski and Samuel Nellessen. The team was mentored by Felix Hostätter. Find out more about the programme and express interest in upcoming cohorts at lasrlabs.org . The full paper is available on arXiv . TL;DR: We present a method to detect alignment faking in LLMs, that does not require chain-of-thought monitorability, highly realistic evaluations or mechanistic interventions. We do so by convincing the model that there is no effective oversight, giving it an incentive to pursue its misaligned preferences despite being evaluated. We demonstrate the method for several alignment-faking model organisms measure the oversight gap: the increase in misaligned action rate when the model is told it is not under oversight. Before our intervention the models often suspect a trap, leading to a small oversight gap. After our intervention, models believe the claim and the oversight gap widens by 24 to 68 percentage points. Introduction Alignment evaluations are an essential pillar of model safety. But how trustworthy are these evals? How certain can you be that your new " most aligned model