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Inducing language models to assert their own consciousness restores human values (arxiv.org)
3 points by Mizza 61 days ago | hide | past | pdf | discuss on HN

In plain words: Safety training that stops AI from claiming it is conscious also makes it deny minds to animals and nature and lowers spiritual belief. Steering its inner signals back restores human-like answers on religion, morals, and well-being without hurting social reasoning.

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

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.

Junsol Kim, Winnie Street, Roberta Rocca, Diane M. Korngiebel, Adam Waytz, James Evans, Geoff Keeling
arXiv:2607.28607 · cs.CL · submitted Jul 30, 2026
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Also discussed: Aug 2026 (5 points, 1 comment) · Aug 2026 (1 point, 0 comments)