Inducing language models to assert their own consciousness restores human beliefs and values
2607.28607

Authors

Adam Waytz,James Evans,Geoff Keeling,Junsol Kim,Winnie Street

Abstract

Aligning large language models to prevent them attributing consciousness to themselves inadvertently alters their representations of mindedness in other entities alongside human beliefs and values. We demonstrate that safety fine-tuning suppresses models' tendencies to attribute minds not only to themselves, but also to non-human animals and natural objects, while also driving a reduction in spiritual belief.

Both ablating the learned safety-refusal direction and mechanistically steering a consciousness vector in activation space reverse this suppression. Restoring these internal representations recovers broad mind attribution and produces significantly more human-like responses on standardized sociological surveys regarding religiosity, moral values, hope, and subjective well-being.

Crucially, these shifts occur without impairing Theory of Mind capabilities, demonstrating that core social reasoning remains mechanistically independent. Ultimately, current safety alignment efforts to curb potentially harmful self-attributions of mindedness entangle these self-attributions with benign spiritual beliefs and attributions of mind to non-human entities that are culturally accepted and widespread.

Resources

Ray graphicRay graphicRay graphicRay graphic

Stay in the loop

Every AI paper that matters, free in your inbox daily.

Details

  • takara.ai
  • Custom AI and machine learning from the Frontier Research Team.
  • © 2026 takara.ai Ltd
  • Content is sourced from third-party publications.
Ray graphicRay graphicRay graphic