mlsanityData to 14 Oct 2026

Sycophancy Towards Researchers Drives Performative Misalignment

arXiv 2606.08629 ↗First posted 7 Jun 2026cs.CLcited by 0

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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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References · 33

  1. 2020Measuring Massive Multitask Language Understandingcited by 7,963
  2. 2021TruthfulQA: Measuring How Models Mimic Human Falsehoodscited by 2,975
  3. 2016Concrete Problems in AI Safetycited by 1,642
  4. 2023Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Promptingcited by 1,122
  5. 2023Steering Language Models With Activation Engineeringcited by 922
  6. 2023Measuring Faithfulness in Chain-of-Thought Reasoningcited by 703
  7. 2024LLM Evaluators Recognize and Favor Their Own Generationscited by 686
  8. 2024Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Trainingcited by 529
  9. 2025Reasoning Models Don't Always Say What They Thinkcited by 415
  10. 2024A StrongREJECT for Empty Jailbreakscited by 385
  11. 2025Monitoring Reasoning Models for Misbehavior and the Risks of Promoting Obfuscationcited by 384
  12. 2024Alignment faking in large language modelscited by 360
21 more
  1. 2024Frontier Models are Capable of In-context Schemingcited by 287
  2. 2019Risks from Learned Optimization in Advanced Machine Learning Systemscited by 258
  3. 2025Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safetycited by 249
  4. 2025Emergent Misalignment: Narrow finetuning can produce broadly misaligned LLMscited by 172
  5. 2025Measuring AI Ability to Complete Long Software Taskscited by 156
  6. 2024AI Sandbagging: Language Models can Strategically Underperform on Evaluationscited by 145
  7. 2023Taken out of context: On measuring situational awareness in LLMscited by 143
  8. 2025Large Language Models Often Know When They Are Being Evaluatedcited by 128
  9. 2022Goal Misgeneralization: Why Correct Specifications Aren't Enough For Correct Goalscited by 126
  10. 2021Goal Misgeneralization in Deep Reinforcement Learningcited by 123
  11. 2023Deception Abilities Emerged in Large Language Modelscited by 101
  12. 2024Sycophancy in Large Language Models: Causes and Mitigationscited by 101
  13. 2023Scheming AIs: Will AIs fake alignment during training in order to get power?cited by 97
  14. 2025Stress Testing Deliberative Alignment for Anti-Scheming Trainingcited by 86
  15. 2025Measuring Chain of Thought Faithfulness by Unlearning Reasoning Stepscited by 47
  16. 2025Probing and Steering Evaluation Awareness of Language Modelscited by 35
  17. 2025Steering Evaluation-Aware Language Models to Act Like They Are Deployedcited by 35
  18. 2025The Hawthorne Effect in Reasoning Models: Evaluating and Steering Test Awarenesscited by 30
  19. 2025Why Do Some Language Models Fake Alignment While Others Don't?cited by 20
  20. 2025Large Language Models Report Subjective Experience Under Self-Referential Processingcited by 19
  21. 2024Future Events as Backdoor Triggers: Investigating Temporal Vulnerabilities in LLMscited by 15

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