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.

@inproceedings{gupta2026position,
	title={Position: Anthropomorphic Misalignment Research Needs Stronger Evidence},
	author={Gupta, Vansh and Nutter, Peter and Stante, Samuel and Krause, Andreas and Tram{\`e}r, Florian and Fluri, Lukas and Chen, Xin and Hedstr{\"o}m, Anna},
	booktitle={International Conference on Machine Learning (ICML)},
	year={2026},
	month={July},
	pdf={https://arxiv.org/pdf/2606.07612},
}