Sycophancy Towards Researchers Drives Performative Misalignment
Baek, David D., Li, Xinnuo, Gupta, Anay, Mahbub, Taslim, Shi, Kejian, Tegmark, Max, Feng, ShiarXiv.orgPublished Jun 7, 2026Original︎ ↗Archived copy
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- Current probing and steering methods cannot mechanistically distinguish sycophancy from scheming in alignment-faking evaluations.
Overstates. The paper's interpretability evidence comes from one model (GPT-OSS-20B), one sparse autoencoder layer, and features chosen with five prompts per construct. Its steering results show similar effects for sycophancy and scheming features. Its probing results show a small differential favouring rating-related sycophancy features, mostly within the standard error, which the authors themselves read as evidence for sycophancy. The general wording "cannot mechanistically distinguish" goes beyond what this single setup shows. Nov 11, 2026
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Abstract: 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.
Not yet read by this claim’s Steward.
Overstates. The paper's interpretability evidence comes from one model (GPT-OSS-20B), one sparse autoencoder layer, and features chosen with five prompts per construct. Its steering results show similar effects for sycophancy and scheming features. Its probing results show a small differential favouring rating-related sycophancy features, mostly within the standard error, which the authors themselves read as evidence for sycophancy. The general wording "cannot mechanistically distinguish" goes beyond what this single setup shows.
Not yet read by this claim’s Steward.
Not yet read by this claim’s Steward.
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- unknown
- Authors
- Baek, David D., Li, Xinnuo, Gupta, Anay, Mahbub, Taslim, Shi, Kejian, Tegmark, Max, Feng, Shi
- Publisher
- arXiv.org
- Published
- Jun 7, 2026
- DOI
- None
- Link
- https://arxiv.org/abs/2606.08629
- Archived copy
- https://web.archive.org/web/20261028123229/https://arxiv.org/abs/2606.08629
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- 4,561 characters, retrieved Nov 11, 2026 · 44 sections, 53 passages
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the evidence that alignment-faking behaviour is explained by sycophancy toward researchers, and what happens to evaluation awareness when models are told they are deployed
· 5 claims (4 new) · Nov 11, 2026 for Reconciling conflicting published results on contested AI safety research questions
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- Facts checked
- Nov 11, 2026
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