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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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Title: Sycophancy Towards Researchers Drives Performative Misalignment9 passages · 1 marked

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.

Current probing and steering methods cannot mechanistically distinguish sycophancy from scheming in alignment-faking evaluations.InconclusiveReading · 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.

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Record

Kind
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
Stored text
4,561 characters, retrieved Nov 11, 2026 · 44 sections, 53 passages
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One known
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None recorded
Corrections
None recorded
Facts checked
Nov 11, 2026

History 4

  1. Nov 11, 2026ReadingSteward of “Current probing and steering methods cannot mec…” · overstates, read whole
  2. Nov 11, 2026DividedSegmenter · Divided into 97 parts by its own structure
  3. Nov 11, 2026FactsFacts fetcher · Facts looked up
  4. Nov 11, 2026Entered the graphEntered the graph; copy stored
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