Large Language Models Report Subjective Experience Under Self-Referential Processing

• URL: http://arxiv.org/abs/2510.24797v2
• Date: Thu, 30 Oct 2025 02:45:50 GMT
• Title: Large Language Models Report Subjective Experience Under Self-Referential Processing
• Authors: Cameron Berg, Diogo de Lucena, Judd Rosenblatt,
• Abstract summary: Large language models sometimes produce structured, first-person descriptions that explicitly reference awareness or subjective experience.<n>We investigate one theoretically motivated condition under which such reports arise: self-referential processing.<n>We test whether this regime reliably shifts models toward first-person reports of subjective experience.
• Score: 0.16623291199400023
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models sometimes produce structured, first-person descriptions that explicitly reference awareness or subjective experience. To better understand this behavior, we investigate one theoretically motivated condition under which such reports arise: self-referential processing, a computational motif emphasized across major theories of consciousness. Through a series of controlled experiments on GPT, Claude, and Gemini model families, we test whether this regime reliably shifts models toward first-person reports of subjective experience, and how such claims behave under mechanistic and behavioral probes. Four main results emerge: (1) Inducing sustained self-reference through simple prompting consistently elicits structured subjective experience reports across model families. (2) These reports are mechanistically gated by interpretable sparse-autoencoder features associated with deception and roleplay: surprisingly, suppressing deception features sharply increases the frequency of experience claims, while amplifying them minimizes such claims. (3) Structured descriptions of the self-referential state converge statistically across model families in ways not observed in any control condition. (4) The induced state yields significantly richer introspection in downstream reasoning tasks where self-reflection is only indirectly afforded. While these findings do not constitute direct evidence of consciousness, they implicate self-referential processing as a minimal and reproducible condition under which large language models generate structured first-person reports that are mechanistically gated, semantically convergent, and behaviorally generalizable. The systematic emergence of this pattern across architectures makes it a first-order scientific and ethical priority for further investigation.

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