mlsanityData to 7 Oct 2026

Position: Anthropomorphic Misalignment Research Needs Stronger Evidence

arXiv 2606.07612 ↗First posted 29 May 2026cs.CY cs.AI cs.LGcited by 6

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Abstract (arXiv)

We argue that many Anthropomorphic Misalignment Research (AMR) studies need stronger evidence to ensure that they can provide a robust foundation for critical safety decisions, such as model deployment and regulation. By evaluating failure modes across different misalignment concepts, such as deception, emergent misalignment, and sycophancy, we show how conceptual ambiguity, non-robust datasets, experimental design, and insufficient causal interventions can lead to overinterpretation of model behaviors. This position paper aims to offer guidance on evidentiary considerations that can help improve methodological rigor in AMR. To achieve this, we provide a clear call to action through a proposed framework of evidence levels and a diagnostic checklist. These shared standards will enable more productive scientific discourse and ensure that claims about AI risks rest on solid empirical foundations.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 60 of its 109 references that are in the map. First posted on arXiv 29 May 2026.

Builds on

60 of 109 references.

References · 60

  1. 2024The Llama 3 Herd of Modelscited by 14,192
  2. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
  3. 2020Measuring Massive Multitask Language Understandingcited by 7,768
  4. 2016Overcoming catastrophic forgetting in neural networkscited by 4,718
  5. 2016Concrete Problems in AI Safetycited by 1,582
  6. 2023Sparse Autoencoders Find Highly Interpretable Features in Language Modelscited by 1,359
  7. 2016Understanding intermediate layers using linear classifier probescited by 1,279
  8. 2023Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!cited by 1,106
  9. 2023Towards Understanding Sycophancy in Language Modelscited by 988
  10. 2024Refusal in Language Models Is Mediated by a Single Directioncited by 960
  11. 2023BeaverTails: Towards Improved Safety Alignment of LLM via a Human-Preference Datasetcited by 828
  12. 2023Steering Language Models With Activation Engineeringcited by 824
38 more
  1. 2022Discovering Language Model Behaviors with Model-Written Evaluationscited by 805
  2. 2023An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuningcited by 622
  3. 2023"Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Modelscited by 457
  4. 2024Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Modelscited by 412
  5. 2024Justice or Prejudice? Quantifying Biases in LLM-as-a-Judgecited by 354
  6. 2024Humans or LLMs as the Judge? A Study on Judgement Biasescited by 314
  7. 2023Role-Play with Large Language Modelscited by 290
  8. 2024Frontier Models are Capable of In-context Schemingcited by 252
  9. 2024Continual Learning of Large Language Models: A Comprehensive Surveycited by 228
  10. 2022Neural Theory-of-Mind? On the Limits of Social Intelligence in Large LMscited by 220
  11. 2023AI Deception: A Survey of Examples, Risks, and Potential Solutionscited by 185
  12. 2023Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the MACHIAVELLI Benchmarkcited by 174
  13. 2025Open Problems in Mechanistic Interpretabilitycited by 173
  14. 2024Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Modelscited by 158
  15. 2024Large Language Models are Inconsistent and Biased Evaluatorscited by 146
  16. 2025Measuring what Matters: Construct Validity in Large Language Model Benchmarkscited by 102
  17. 2022Is Power-Seeking AI an Existential Risk?cited by 101
  18. 2025Model Organisms for Emergent Misalignmentcited by 84
  19. 2024Me, Myself, and AI: The Situational Awareness Dataset (SAD) for LLMscited by 78
  20. 2024Truth is Universal: Robust Detection of Lies in LLMscited by 78
  21. 2023Still No Lie Detector for Language Models: Probing Empirical and Conceptual Roadblockscited by 77
  22. 2025Persona Features Control Emergent Misalignmentcited by 76
  23. 2025A Survey on Sparse Autoencoders: Interpreting the Internal Mechanisms of Large Language Modelscited by 73
  24. 2025Convergent Linear Representations of Emergent Misalignmentcited by 66
  25. 2025The MASK Benchmark: Disentangling Honesty From Accuracy in AI Systemscited by 57
  26. 2025Thought Crime: Backdoors and Emergent Misalignment in Reasoning Modelscited by 57
  27. 2024Learning to Ask: When LLM Agents Meet Unclear Instructioncited by 38
  28. 2023Engagement, User Satisfaction, and the Amplification of Divisive Content on Social Mediacited by 30
  29. 2024Language Models Represent Beliefs of Self and Otherscited by 29
  30. 2025To Steer or Not to Steer? Mechanistic Error Reduction with Abstention for Language Modelscited by 26
  31. 2025Multi-turn Evaluation of Anthropomorphic Behaviours in Large Language Modelscited by 25
  32. 2024The Reasons that Agents Act: Intention and Instrumental Goalscited by 23
  33. 2024Relational Composition in Neural Networks: A Survey and Call to Actioncited by 23
  34. 2025Benchmarking Deception Probes via Black-to-White Performance Boostscited by 17
  35. 2025Emergent Misalignment via In-Context Learning: Narrow in-context examples can produce broadly misaligned LLMscited by 17
  36. 2025Lessons from a Chimp: AI "Scheming" and the Quest for Ape Languagecited by 16
  37. 2025Incomplete Tasks Induce Shutdown Resistance in Some Frontier LLMscited by 16
  38. 2024From Imitation to Introspection: Probing Self-Consciousness in Language Modelscited by 14

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 6.

6 papers.

  1. 2026Data Attribution of Emergent Misalignment with Persona Featurescited by 1
  2. 2026Harmful Content Is Not Enough: Continuation Framing Moderates In-Context Emergent Misalignmentcited by 0
  3. 2026Causal Behavioral Evaluation of AI Agents at Scale via Automated Behavioral Sciencecited by 0
  4. 2026Shutdown Sabotage Propensities in Multi-Agent Systemscited by 0
  5. 2026Correct, Don't Delete: Mitigating Emergent Misalignment with Corrective Supervisioncited by 0
  6. 2026The Unequal Influence of Bad Advice: Using Training Data Attribution to Modulate Emergent Misalignmentcited by 0