arXiv — NLP / Computation & Language · · 3 min read

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

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Computer Science > Computation and Language

arXiv:2607.28607 (cs)
[Submitted on 30 Jul 2026]

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

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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.
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2607.28607 [cs.CL]
  (or arXiv:2607.28607v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2607.28607
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Junsol Kim [view email]
[v1] Thu, 30 Jul 2026 17:57:10 UTC (2,721 KB)
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