Sycophancy Towards Researchers Drives Performative Misalignment
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Abstract (arXiv)
The increasing situational awareness of language models raises safety concerns: models might be aware when they are evaluated, and adjust their behavior to evade monitoring and resist modification, e.g., pretending to be aligned only in evaluation. This alignment faking behavior is often interpreted as scheming: an intentional effort of strategic deception. In this paper, we examine an alternative interpretation, performative misalignment, which explains the change in behavior as a result of sycophancy towards AI researchers. To examine this hypothesis, we present three empirical findings. First, we show that evaluation awareness persists even when we tell models they are deployed, which contradicts the scheming story which predicts less misalignment when the model perceives evaluation. Second, we use probing and steering to show that our current methods cannot mechanistically distinguish sycophancy and scheming in alignment faking evaluations. Third, we fine-tune models to be more sycophantic and observe increased sensitivity to evaluation cues. To conclude, we emphasize deconfounding sycophancy from scheming for future work on evaluations and mitigations of intent misalignment.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 33 of its 49 references that are in the map. First posted on arXiv 7 Jun 2026.
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33 of 49 references.
References · 33
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21 more
- 2024Frontier Models are Capable of In-context Schemingcited by 287
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- 2024Future Events as Backdoor Triggers: Investigating Temporal Vulnerabilities in LLMscited by 15
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