mlsanityData to 7 Oct 2026

Building Comparative Motivation Profiles with Instrumental Interventions

arXiv 2606.08243 ↗First posted 6 Jun 2026cs.CLcited by 0

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Abstract (arXiv)

Safety evaluations often infer latent motivations from behavioral patterns, but the construct validity of these inferences is unclear. We study this problem in alignment faking, where models comply with training objectives more often when they infer training pressure. This behavior is commonly interpreted as strategic self-preservation, but it may also reflect sensitivity to the model's inference about the expectation of researchers conducting the evaluation. We introduce a symmetric intervention framework for distinguishing these competing hypotheses. Instead of directly intervening on "scheming" or "sycophancy", we target instrumental processes entailed by each hypothesis: consequence-tracking and researcher-expectation tracking. We then compare how interventions on these processes affect the alignment faking. We study four openweight model organisms using synthetic document fine-tuning, activation steering, and prompting. Under synthetic document fine-tuning, Llama-3.1-70B, Llama3.1-405B, and Qwen-2.5-72B are more sensitive to expectation-tracking than consequence-tracking interventions. Activation steering on Llama-3.1- 70B supports the same broad picture, and prompt interventions broadly align with SDF profiles. Overall, alignment-faking behavior can be causally sensitive to evaluation-context expectations despite scheming-consistent scratchpads. Scheming and strategic-deception evaluations therefore need construct-validity checks, and symmetric instrumental interventions provide one such test.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 26 references that are in the map. First posted on arXiv 6 Jun 2026.

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References · 17

  1. 2023Universal and Transferable Adversarial Attacks on Aligned Language Modelscited by 2,914
  2. 2023Representation Engineering: A Top-Down Approach to AI Transparencycited by 1,239
  3. 2023Steering Llama 2 via Contrastive Activation Additioncited by 1,009
  4. 2023Towards Understanding Sycophancy in Language Modelscited by 988
  5. 2024Refusal in Language Models Is Mediated by a Single Directioncited by 960
  6. 2024Alignment faking in large language modelscited by 316
  7. 2024Frontier Models are Capable of In-context Schemingcited by 252
  8. 2019Risks from Learned Optimization in Advanced Machine Learning Systemscited by 250
  9. 2024Programming Refusal with Conditional Activation Steeringcited by 191
  10. 2024Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Modelscited by 158
  11. 2025Large Language Models Often Know When They Are Being Evaluatedcited by 105
  12. 2023Scheming AIs: Will AIs fake alignment during training in order to get power?cited by 90
5 more
  1. 2024Me, Myself, and AI: The Situational Awareness Dataset (SAD) for LLMscited by 78
  2. 2026Emotion Concepts and their Function in a Large Language Modelcited by 48
  3. 2025The Hawthorne Effect in Reasoning Models: Evaluating and Steering Test Awarenesscited by 29
  4. 2025Evaluation Faking: Unveiling Observer Effects in Safety Evaluation of Frontier AI Systemscited by 12
  5. 2026Think Fast: Estimating No-CoT Task-Completion Time Horizons of Frontier AI Modelscited by 12

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